Pan-cancer analysis of PSCA that is associated with immune infiltration and affects patient prognosis

Prostate stem cell antigen (PSCA) is associated with disease progression, promotion of angiogenesis, invasion, metastasis and immune evasion in cancer. However, its expression pattern and diagnostic and prognostic potential have not been thoroughly analysed from a pan-cancer perspective. This study aimed to examine the effects of PSCA on the prognosis and inflammatory cell infiltration patterns of various cancer types. We analysed the relationship between PSCA expression and immunological subtypes in tumor microenvironment (TME) and the role of molecular subtypes, potentially promising immune biomarkers and tumour-infiltrating lymphocytes (TILs) in various cancer types, especially lung adenocarcinoma (LUAD). In addition, we investigated the prognostic significance of PSCA expression in LUAD. The co-expression network of PSCA was found to be mainly involved in the regulation of immune responses and antigen processing and expression and was significantly enriched in pathological and substance metabolism-related pathways in cancer. Altogether, this study reveals that PSCA is a promising target for immunotherapy in patients with cancer.


Introduction
The PSCA gene is associated with disease progression, promotion of angiogenesis, invasion, metastasis and avoidance of immune surveillance in cancer [1,2].For example, it has been shown that PSCA expression is elevated in pancreatic and gastric cancers.However, its expression pattern and diagnostic and prognostic potential across cancers remain elusive [3,4].This study aimed to reveal the role of PSCA in the diagnosis and prognosis of various cancer types, especially LUAD, primarily based on The Cancer Genome Atlas (TCGA) data.In addition, dysregulation of PSCA was examined from a pan-cancer perspective.Previous studies have validated that patients with cancer tend to produce autoantibodies.Tissue-specific antigens may serve as a target for adoptive T-cell transfer-based immunotherapy.The body produces antibodies by inducing inflammation or increasing the production of self-antigens.Owing to the presence of antibodies in the early stages of the disease and the relative availability of serum samples, antibody diversity plays an important role in the diagnosis and prognosis of tumours in clinical settings.In addition, the presence of antibodies indicates the relative immunogenicity of an antigen.Therefore, analysing the response of tissue-specific antigens and their antibodies is necessary for detecting the efficacy of immunotherapy.
However, the expression pattern as well as the diagnostic and prognostic potential of PSCA have not been analysed comprehensively from a pan-cancer perspective.In this study, we examined the effects of PSCA on the prognosis and immune landscape of cancer.In addition, we investigated the potential relationship between PSCA expression and immunological subtypes of TME [11,12] and the role of molecular subtypes, promising biomarkers of immunity [13] and tumour-infiltrating lymphocytes (TILs) in various cancer types, especially LUAD [14].In particular, we examined the effects of PSCA expression on the prognosis of LUAD.Altogether, this study highlights the role of PSCA in immunotherapy and may guide the development of novel therapeutic strategies for cancer [15,16].

Data sources and collection
RNA expression and clinical data were extracted from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects from the UCSC Xena database (https:// xenabrowser).Data on DNA copy number and methylation were obtained from the cBioPortal database (https://www.cbioportal.org/).

Mutation profiles
The cBioPortal for Cancer Genomics (http://www.cbioportal.org) is a repository of large-scale cancer genomics datasets.The Cancer Somatic Mutation Inventory (https://cancer.sanger.ac.uk/cosmic/) [17] is used for determining the impact of somatic mutations on cancer [18].

Correlation analysis
PSCA expression was analysed at the chromosomal level, and its correlation with other variables was examined in various cancer types [18].Pearson analysis was used to assess the correlation of PSCA with immune checkpoints [19] and mismatch repair (MMR) proteins [20].The 'pheatmap' package in R was used to generate a heat map for visualising the results [15,21].

Immune infiltration
The tumour purity of 33 human cancers was evaluated using the 'ESTIMATE' package [1,11].The relationship between PSCA expression and tumour purity scores in various cancer types was visualised on a scatter plot.The Tumor Immune Estimation Resource 2.0 (TIMER2.0;http://timer.cistrome.org/)web server is used for systematic analysis of immune infiltration.The differential expression of PSCA between tumour and adjacent normal tissues was analysed, and the relationship between PSCA expression and immune infiltration was examined using several immune-related deconvolution algorithms [11,22].TISIDB [23] (http://cis.hku.hk/TISIDB/) was used to examine whether PSCA expression was different between patients who responded and did not respond to immunotherapy.In addition, we assessed the correlation between PSCA expression and markers of immune cell subsets.

Statistical analysis
All data are expressed as the mean ± standard deviation (SD).Differences between groups were analysed using Student's t-test.In addition, the correlation in the scatter plot was evaluated using Spearman's rank correlation coefficient.Furthermore, the survival difference between groups in the survival curves were analysed by the Log-rank test.Statistical analysis was performed using the R (version 3.6.2) software.A p-value of <0.05 (two-tailed) was considered statistically significant.

Real-time PCR
Real-time reverse transcription polymerase chain reaction (qRT-PCR) was performed as described in our previous study [24,25].The primer sequences used for PCR are as follows:

Western blotting
Tumour and normal tissues were lysed in RIPA buffer (Solarbio, China) and denatured at 100˚C for 15 min.Extracted proteins were separated on 10% sodium dodecyl sulphate-polyacrylamide gels and transferred to polyvinylidene difluoride (PVDF) membranes.The membranes were blocked with 5% skim milk for 1 h and incubated overnight with primary antibodies, including anti-PSCA (1:200, Proteintech, 17171-1-AP) and anti-GADPH (1:5000, Proteintech, 60004-1-Ig) antibodies.The following day, the membranes were incubated with secondary antibodies for 2 h at ambient temperature, and protein bands were detected using an ECL kit (Billerica Millipore, USA).This study was approved by the Human Ethics Committee of the Affiliated Hospital of Nantong University (No. 2018-K020), and written informed consent was obtained from all patients.

Transwell assay
Transwell assay was performed to assess cell migration and invasion.Briefly, cells (5 × 104) were inoculated in Matrigel-coated (for invasion) or uncoated (for migration) Transwell chambers.Serum-free medium (SFM) was added to the upper chamber, whereas DMEM was added to the lower chamber.After 24 hours of incubation, the cells were stained with 0.1% crystal violet and counted under a microscope.

Flow cytometry
The cell cycle and apoptosis were analysed via flow cytometry according to the manufacturer's instructions.To examine the cell cycle, cells were harvested using trypsin and resuspended in PBS at a concentration of 1 × 105 cells/100 μL.Subsequently, the cells were stained with propidium iodide (PI) on ice for 30 min, washed with PBS and detected on a BD FACSCalibur Flow Cytometer (USA).The distribution of cells within the cell cycle was analysed using the ModFit software.For the detection of apoptosis, cells were washed, resuspended in pre-chilled PBS and stained with Annexin V-647 and PI solution in the dark.After 15 minutes of incubation, apoptosis was detected via flow cytometry.

Expression and mutation patterns of PSCA in pan-cancer
The .The results suggested that PSCA was significantly upregulated in UCEC, BRCA and ESCA and significantly downregulated in GBM, KIRP, PRAD, STAD, HNSC, KIRC and READ.In addition, multi-omic analysis of expression data of LUAD samples validated that multiple differentially expressed genes, including PSCA, contributed to the increased risk of disease recurrence.However, the prognostic potential of PSCA in LUAD remains unclear.Therefore, we performed in-depth multi-omic analysis based on TCGA-LUAD dataset.A waterfall plot was generated to analyse the mutation pattern of PSCA in LUAD, and differences in the mutation frequency of PSCA among samples were estimated via chi-square test (Fig 1D).Subsequently, the level 4 single-nucleotide variant data of all samples in TCGA-LUAD dataset, which were processed by MuTect2, were integrated, and protein domain information was acquired using the 'maftools' R package (Fig 1E).

Comprehensive analysis of tumour stemness index of PSCA in LUAD
We obtained six tumour stemness indices based on mRNA expression and methylation from previous studies and analysed the correlation among them20 (

Differential analysis of pathological features in pan-cancer
Histograms  types.BRCA showed significant differences in tumour stage, grade and other malignant characteristics.The genome-wide correlation of PSCA with other clinicopathological features in LUAD, GBM, STAD, OV, LUSC and BRCA is demonstrated in S4 Fig.

Correlation analysis of immune checkpoints in pan-cancer
The expression of PSCA and 150 markers of five immune pathways (chemokines, receptors, MHC molecules, immunosuppressors and immunostimulators) was examined, and the correlation between PSCA and the marker genes was visualised on a heat map (Fig 4A).The expression of MHC molecules, receptors, immunosuppressors, immunostimulators and chemokine-related genes was compared between high-and low-PSCA-expression groups in

Univariate and multivariate regression analyses of the prognostic potential of PSCA in LUAD
Univariate and multivariate regression analyses were performed to identify independent factors influencing the prognosis and survival of patients with LUAD.A forest plot was generated to demonstrate PSCA expression and clinicopathological data, including pathological stages, A logistic regression model was established to evaluate the relationship between PSCA expression and clinicopathological features such as N stage and residual tumour (S3 Table ).Consistent with the Cox model, the logistic regression model demonstrated a strong correlation between the two clinicopathological features (N1, N2, N3 and N0) and PSCA expression.

Effects of PSCA expression on prognosis in LUAD
To examine the prognostic significance of PSCA in LUAD, survival analysis was performed in subgroups based on different clinicopathological characteristics.High expression of PSCA was found to be significantly associated with poorer survival in subgroups based on T stage, N stage and primary treatment outcomes (PR and CR groups) (Fig 5A -5C).Based on the survival status and OS time of patients with LUAD, the 1-, 3-and 5-year survival probabilities were assessed using time-dependent ROC curves [26].The results showed that AUC values at 1, 2 and 3 years were 0.584, 0. In LUAD, high PSCA expression was significantly associated with advanced pathological stage and grade and poor OS rates (Fig 5E).PD and SD in primary therapy outcome subgroup, pathologic stage III and IV subgroup, T2 and T3 and T4 subgroup in T stage, and M1 subgroup in M stage were significantly overexpressed in poor prognosis subgroup (Fig 5F -5I).In addition to verifying the differential efficacy of PSCA for LUAD (AUC = 0.549, CI = 0.497−0.600)(S9A Fig) , we examined the efficacy of PSCA in distinguishing the abovementioned clinicopathological features.PSCA had better discrimination power for TNM stage, residual tumour and primary treatment outcomes (S9B-S9F Fig), with the identification of residual tumour being particularly significant (AUC = 0.609, CI = 0.469−0.749).These results suggest that high PSCA expression is closely related to the poor prognosis of LUAD.

ESTIMATE algorithm base on PSCA expression infers tumour purity in LUAD
The ESTIMATE algorithm was used to investigate the immune infiltration landscape of LUAD.The top 3 important correlations between PSCA and immune cells were visualised on scatter plots (Fig 6A -6C).PSCA expression was negatively correlated with stromal, immune and estimate scores in KIPAN and BLCA, indicating that samples with low immune infiltration levels had high PSCA expression.These results are consistent with those described in previous sections, indicating that patients with high PSCA expression have a poor prognosis.The TIMER algorithm was used to assess the correlation between immune cell infiltration and PSCA expression in LUAD and LUSC, and the results were visualised on a scatter plot (Fig 6D and Table 2).PSCA expression was significantly positively correlated with the abundance of Th cells, neutrophils, macrophages and DCs in LUAD.However, the correlation between PSCA expression and immune cell infiltration was not significant in LUSC.

Immune checkpoint gene expression and immune infiltration analyses
We identified key immune checkpoint genes reported in the literature and found that the genes were significantly associated with PSCA (Fig 7A)  PSCA was found to be enriched in the C1 and C6 subtypes, suggesting that PSCA affects the immune microenvironment of LUAD through these two pathways.

GO and KEGG enrichment analyses
GO [27] functional annotation and KEGG [28] pathway enrichment analyses (Fig 8 and Table 3) indicated that PSCA is closely related to substance metabolism, enzyme inhibitor activity, cell proliferation, structural components of the cytoskeleton and dynein complex binding.The results of GO and KEGG analyses were visualised on histograms and bubble  8E).In particular, GO analysis revealed that PSCA was enriched in biological processes such as cornification, antimicrobial humoral response, cilium movement, antimicrobial humoral immune response mediated by antimicrobial peptides and keratinocyte differentiation; molecular functions such as glucuronosyltransferase activity, serine-type endopeptidase inhibitor activity, structural constituent of the cytoskeleton, dynein complex binding and endopeptidase inhibitor activity and cellular components such as the axoneme, ciliary plasm, motile cilium, nucleosome and DNA packaging complex.KEGG analysis revealed that genes co-expressed with PSCA were mostly enriched in pathways associated with the metabolism of xenobiotics by cytochrome P450, chemical carcinogenesis, ascorbate and aldarate metabolism, alcoholism and pentose and glucuronate interconversion (Fig 8C , 8D and 8F).

PSCA promotes cell migration and invasion in lung cancer
Western blotting and qRT-PCR showed that the expression of PSCA was higher in lung cancer tissues than in para-cancerous tissues (Fig 9A and 9B).Subsequently, the expression of PSCA was examined in four lung cancer cell lines.The expression of PSCA was higher in H1299 and A549 cells (Fig 9C and 9D).Three siRNAs were used to knock down PSCA in H1299 and A549 cells, with si-PSCA-1 having the most significant effect (Fig 9E and 9F).PSCA knockdown attenuated the migratory and invasive capabilities of H1299 and A549 cells (Fig 9G and  9H) and enhanced apoptosis (Fig 9I and 9K).In addition, PSCA knockdown prompted lung cancer cells to exit the cell cycle (Fig 9J and 9L).

Discussion
PSCA is associated with disease progression, promotion of angiogenesis, invasion, metastasis and immune evasion in cancer [4,29].However, its expression pattern as well as diagnostic and prognostic potential have not been thoroughly analysed from a pan-cancer perspective  https://doi.org/10.1371/journal.pone.0298469.g009[8].In this study, we examined the expression and prognostic value of PSCA in pan-cancer using the expression data of 33 cancer types from TCGA and GTEx databases.PSCA expression was upregulated in UCEC, BRCA and ESCA and downregulated in GBM, KIRP, PRAD, STAD, HNSC, KIRC and READ.Multi-omic analysis of LUAD data revealed that multiple differentially expressed genes, including PSCA, were associated with an increased risk of disease recurrence.The frequency of deep amplifications in PSCA was high in LUAD samples, which supported the results of the multi-omic analysis.PSCA expression was positively correlated with ENHss, EREG-METHss and DNAss.In addition, the correlation between the expression of PSCA and genes related to RNA modification (m1A, m5C and m6A), including NSUN5, DNMT3A, DNMT1, TRDMT1, HNRNPC and IGF2BP1, was positive in CHOL, UCS, PAAD, ACC, LIHC, GBM, OV and SKCM but negative in STAD, STES, KICH, KIPAN and KIRC.In addition, PSCA expression was significantly associated with specific molecular subtypes, tumour stage, grade, OS and immune cell subtypes in pan-cancer.Altogether, these findings suggest that PSCA expression varies across different clinicopathological characteristics of tumours, such as pathological stages and grades.
Cox regression analysis suggested that high PSCA expression was associated with a high risk of BRCA, LUAD, GBM, KIRC, READ and PAAD, whereas low PSCA expression was associated with a high risk of CESC, HNSC, SKCM and DLBC.KM curves demonstrated that low PSCA expression was associated with a poor prognosis in GBM, SKCM, CESC, HNSC and BRCA, whereas high PSCA expression was associated with a poor prognosis in KIRC, PAAD and READ in TCGA datasets.In addition, high PSCA expression was associated with poor prognosis and survival in LUAD, ovarian cancer and colorectal cancer in GEO datasets.Subgroup analysis based on clinicopathological characteristics showed that PSCA was significantly correlated with tumour stage, grade and TNM stage in CESC, HNSC, PSCA, GBMLGG, BRCA, SKCM, COAD, THYM, READ and BLCA (S6G Fig) .To determine the immunological mechanisms through which PSCA affects cancer development and progression, we analysed the expression data of 150 marker genes for five immune pathways (chemokines, receptors, MHC molecules, immunosuppressors and immunostimulators) [13,14,30].The results suggested that PSCA affects cancer development and progression through immunostimulatory factors [30].
The prognostic role of PSCA in LUAD was analysed based on pathological stage, TNM stage and other clinicopathological features.Univariate and multivariate regression analyses validated that N stage and primary treatment outcomes were independent factors affecting the prognosis and survival of patients with LUAD.Nomograms and calibration curves were plotted to quantify and verify the prognostic value of each clinical variable.The results suggested that high PSCA expression was significantly associated with poorer OS.Time-dependent ROC curves demonstrating the 1-, 3-and 5-year OS probabilities of patients with LUAD validated the ability of PSCA to distinguish poor prognostic clinicopathological subgroups.High PSCA expression was significantly associated with advanced pathological stages and grades, poor OS rates and worse prognosis.
To investigate the immune infiltration landscape of LUAD, the ESTIMATE algorithm was used to calculate tumour purity scores based on PSCA expression.Subsequently, the CIBER-SORT and TIMER algorithms were used to evaluate the correlation between immune cell infiltration and PSCA expression.In tumour types such as LUAD, BLCA and KIRP, high PSCA expression was significantly negatively correlated with the abundance of various immune cell types, suggesting that patients with high PSCA expression had low immune cell infiltration, which is consistent with the overall poor prognosis.The differential expression of PSCA among the six immune subtypes suggested that the immune microenvironment of LUAD is influenced by mechanisms related to wound healing, IFN-γ dominance and TGF-β dominance.Similarly, GO and KEGG enrichment analyses of genes co-expressed with PSCA and GSEA of PSCA showed that PSCA was significantly enriched in disease pathology-and substance metabolism-related pathways.
In conclusion, our study reveals the differential expression and prognostic value of PSCA in different tumour tissues, which provides assistance in the clinical diagnosis and assessment of tumours.Meanwhile, we found that PSCA is closely associated with the immune infiltration status of tumours, and this may make it a new target for immune intervention for the treatment of tumours.

Conclusions
Elevated PSCA expression may affect the prognostic value of pan-cancer by changing the degree of immune infiltration.Especially in LUAD, high PSCA expression was associated with worse survival outcomes and lower immune cell infiltration.We performed a comprehensive assessment of PSCA, revealing its potential role as a patient prognostic indicator and its immunomodulatory role.

Fig 1 .
Fig 1.Expression and mutation patterns of PSCA in pan-cancer.(A) Violin plot demonstrating differences in copy number variations between groups; (B) Differential expression of PSCA between unpaired tumour and normal tissue samples; (C) Differential expression of PSCA between paired tumour and normal tissue samples in combined TCGA and GTEx dataset; (D) Waterfall plot demonstrating the mutation frequency of PSCA in TCGA-LUAD dataset; I Domain information of single-nucleotide variants in TCGA-LUAD dataset; (F) Heat map of the correlation among six tumour stemness indices based on mRNA expression and methylation.https://doi.org/10.1371/journal.pone.0298469.g001 were generated to demonstrate specific molecular subtypes (S3A Fig), tumour stages (S3B Fig), OS rates (S3C Fig), immune cell subtypes (S3D Fig), grades (S3E Fig) and mutation difference between responders and non-responders (S3F Fig) in different cancer
/doi.org/10.1371/journal.pone.0298469.t001LUAD dataset using TISIDB, and the results were visualised on a heat map.Differences in the expression of immunostimulators between the two groups were most predominant (Fig 4B-4F).S7 Fig shows the gene expression valueICNA and methylation levels of lymphocytes.The expression of immunosuppressors, immunostimulators and MHC molecules was evaluated based on PSCA expression in each sample.

Fig 3 .
Fig 3. Prognostic analysis of PSCA in TCGA, TARGET and GTEx databases in pan-cancer.(A) Log-rank test was used to obtain a significant forest plot of the risk of pan-cancer overall survival prognosis to quantify the hazard ratio and significance of PSCA in pan-cancer; (B-F) Low PSCA expression was associated with a poor prognosis in GBM, SKCM, CESC, HNSC and BRCA; (G-I) High PSCA expression was associated with a poor prognosis in KIRC, PAAD and READ.https://doi.org/10.1371/journal.pone.0298469.g003

Fig 4 .
Fig 4. PSCA affects immune regulation in tumours.(A) Expression of PSCA and five types of marker genes in each sample; (B-F) Heat map demonstrating the differential expression of MHC molecules, receptors, immunosuppressors, immunostimulators and chemokine-related genes between the high-and low-PSCA-expression groups in LUAD.https://doi.org/10.1371/journal.pone.0298469.g004 579 and 0.550, respectively (Fig 5D).To examine the ability of PSCA to distinguish unfavourable clinicopathological characteristics in LUAD, PSCA expression was compared between/among subgroups based on T stage, N stage, M stage, treatment outcomes and pathological stage (Fig 5E-5I) and clinical variables Subgroup ROC curves (S9 Fig).

Fig 6 .
Fig 6.Stromal, immune and ESTIMATE scores were calculated for each patient in each tumour dataset based on PSCA expression.(A-C) Scatter plots demonstrating the three most significant correlations; (D) Scatter plots demonstrating the correlation between PSCA expression and the abundance of B cells, CD4 T cells, CD8 T cells, neutrophils, macrophages and DC in LUAD and LUSC as assessed using the TIMER algorithm.https://doi.org/10.1371/journal.pone.0298469.g006 . The abundance of NK cells, CD56dim NK cells, neutrophils, mast cells, Treg cells, Th2 cells, Th1 cells, Tgd cells, Tcm cells, macrophages, aDCs and eosinophils was closely associated with the expression of PSCA in LUAD (Fig 7B).Similarly, PSCA expression was significantly correlated with the degree of infiltration of immune cells in other cancer types (Fig 7C).The correlation between the expression of PSCA and abundance of tumour-infiltrating immune cells was visualised on a lollipop chart (Fig 7D).PSCA expression was negatively correlated with the abundance of Tem and T helper cells (Fig 7F and 7G) and significantly positively correlated with that of Tgd cells, neutrophils and CD56bright NK cells (Fig 7H-7J).To characterise molecular mechanisms in the immune microenvironment, we analysed the relationship between PSCA and six immune subtypes,

Fig 7 .
Fig 7. Analysis of immune checkpoint gene expression and immune cell infiltration.(A) Heat map demonstrating the correlation between PSCA and immune checkpoint genes; (B) The CIBERSORT algorithm was used to estimate the proportion of 22 immune cell types.Box plot demonstrates differences in the abundance of immune cells between the high-and low-PSCA-expression groups; (C) 22 types of immune cell infiltration in PSCA cancer (*, P < 0.05; **, P < 0.01; ***, P < 0.001); (D) Lollipop chart demonstrating the correlation between PSCA expression and immune cell infiltration in ID; (E) Relationship between PSCA expression and immune and molecular subtypes in LUAD; (F-G) Scatter plot demonstrating a negative correlation between PSCA expression and the abundance of Tem and T helper cells; (H-J) Scatter plot demonstrating a significantly positive correlation between PSCA expression and the abundance of Tgd cells, neutrophils and CD56bright NK cells.https://doi.org/10.1371/journal.pone.0298469.g007

Fig 8 .
Fig 8. GO and KEGG enrichment analyses of genes co-expressed with PSCA.(A-B) Histogram and bubble plot of GO analysis; (C-D) Histogram and bubble plot of KEGG analysis; (E-F) Network map of GO and KEGG enrichment analyses.https://doi.org/10.1371/journal.pone.0298469.g008

Fig 9 .
Fig 9. PSCA expression is upregulated in tumour tissues and promotes tumour cell migration, invasion and proliferation.(A-B) Western blotting and qRT-PCR were performed to evaluate the mRNA and protein expression of PSCA in six pairs of tumour tissues (T) and adjacent normal tissues (N); (C-D) Protein and mRNA expression of PSCA in four lung cancer cell lines; (E-F) Quantitative analysis of the expression of PSCA in control and PSCA-knockdown H1299 and A549 cells; (G) Transwell assay was performed to assess cell migratory and invasion in the negative control and PSCA-knockdown groups; (H) Quantitative analysis of migrating and invading tumour cells; (I-L) Flow cytometric detection of apoptotic cells and cell cycle in control and PSCAknockdown cells (n.s, p-value>0.05;*, p-value<0.05;**, p-value<0.01;***, p-value<0.001;****, p-val = e<0.0001).

Table 1
).A forest plot was generated to evaluate the hazard ratio and significance of PSCA in pan-cancer (Fig3A).Survival curves demonstrated that low PSCA expression was associated with a poor prognosis in GBM, SKCM, CESC, HNSC and BRCA (Fig 3B-3F), whereas high PSCA expression was associated with a poor expression in KIRC, PAAD and READ (Fig 3G-3I).The impact of PSCA on prognosis was assessed in multiple GEO datasets.PSCA was significantly associated with prognosis in LUAD, ovarian cancer and colorectal cancer.In particular, high PSCA expression was associated with poor OS in LUAegbcob-00182-UM dataset: cox.
Table shows the results of survival analysis in multiple datasets.